ReviewCancers2024
Risk Prediction Models for Oral Cancer: A Systematic Review.
Review in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 19 citations in OpenAlex.
- Predictors of Lesion Detection in a 12-Year Oral Cancer Screening Program: A Cohort Study in Northern Portugal.Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology · 2026Article
- Development and Validation of an Interpretable Machine Learning Model for Predicting Distant Metastasis in Tongue Squamous Cell Carcinoma: A Multicentre Study.International dental journal · 2026Article
- Risk classification of oral potentially malignant disorders (OPMDs) using explainable machine learning through a community-based cross-sectional study in rural India.BMC public health · 2026Article
- Oral cancer risk stratification: A cross-sectional population-based screening study in Northeast India.International journal of cancer · 2026Article
- Immunological Analysis of Oral Cytobrush Specimens for Early Detection of Oral Cancer Biomarkers: A Comprehensive Review.International journal of molecular sciences · 2026Review
- Predicting health-related quality of life two years post-diagnosis across seven cancer types: using machine learning to identify vulnerable patients.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Article
- Risk prediction models for oral squamous cell carcinoma and oral potentially malignant disorders in Asian populations: a systematic review and meta-analysis.Frontiers in oral health · 2026Article
- Exploring confidence in recognizing oral cancer among dentists and dental students.Clinical oral investigations · 2025Article
- Development and validation of a multivariable model to identify candidates for oral cancer screening in Nigeria.Communications medicine · 2025Article
- Beyond Genetics: Exploring Lifestyle, Microbiome, and Social Determinants in Oral Cancer Development.Cancers · 2025Review
- Clinical prediction models using machine learning in oncology: challenges and recommendations.BMJ oncology · 2025Review
- Integrating Genetic Insights and Artificial Intelligence for Enhanced Oral and Maxillofacial Cancer Care.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Leveraging Autofluorescence for Tumor Detection, Diagnosis, and Accurate Excision with Surgical Margin Assessment in Tumor Excision.Dentistry journal · 2024Article
- Review
- A Roadmap for the Rational Use of Biomarkers in Oral Disease Screening.Biomolecules · 2024Review
- The impact of tumor budding and single-cell invasion on survival in patients with stage III/IV locally advanced oral squamous cell carcinoma- results from a prospective cohort study.Frontiers in oncology · 2024Article
- Risk Factors Associated with Oral Cancer: A Hospital-based Case-control Study in Telangana State, India.Contemporary clinical dentistryArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
In the last 30 years, there has been an increasing incidence of oral cancer worldwide. Earlier detection of oral cancer has been shown to improve survival rates. However, given the relatively low prevalence of this disease, population-wide screening is likely to be inefficient. Risk prediction models could be used to target screening to those at highest risk or to select individuals for preventative interventions. This review (a) systematically identified published models that predict the development of oral cancer and are suitable for use in the general population and (b) described and compared the identified models, focusing on their development, including risk factors, performance and applicability to risk-stratified screening. A search was carried out in November 2022 in the Medline, Embase and Cochrane Library databases to identify primary research papers that report the development or validation of models predicting the risk of developing oral cancer (cancers of the oral cavity or oropharynx). The PROBAST tool was used to evaluate the risk of bias in the identified studies and the applicability of the models they describe. The search identified 11,222 articles, of which 14 studies (describing 23 models), satisfied the eligibility criteria of this review. The most commonly included risk factors were age (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.